Authors:
Sarra Bouzayane
1
and
Inès Saad
2
Affiliations:
1
MIS Laboratory, University of Picardie Jules Verne, 33 rue Saint-Leu, 80039 Amiens and France
;
2
MIS Laboratory, University of Picardie Jules Verne, 33 rue Saint-Leu, 80039 Amiens, France, Amiens Business School, 18 Place Saint-Michel, 80038 Amiens and France
Keyword(s):
Recommender System, Knowledge Sharing, Learning Process, Leader Learner, MOOC, Periodic Incremental Prediction.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Education and Training
;
Health Engineering and Technology Applications
;
Knowledge-Based Systems
;
Simulation and Modeling
;
Simulation Tools and Platforms
;
Symbolic Systems
Abstract:
This paper focuses on the support process, within a Massive Open Online Course (MOOC), that is currently unsatisfactory because of the very limited size of the pedagogical team compared to the massive number of the enrolled learners who need support. Indeed, many of the MOOC learners can not appropriate the information they receive and must therefore be assisted in order to not abandon the course. Thus, to help these learners take advantage of the course they follow, we propose a tool to recommend to each of them an ordered list of “Leader learners” who are able to support him throughout his navigation in the MOOC environment. The recommendation phase is based on a multicriteria decision making approach to weekly predict the set of “Leader learners”. Moreover, since the MOOC learners’ profiles are very heterogeneous, we recommend to each of them the leaders who are most appropriate to his profile in order to ensure a good understanding between them. The recommendation we propose is v
alidated on real data coming from a French MOOC and has proved satisfactory results.
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